TEASER: Fast and Certifiable Point Cloud Registration
Heng Yang, Jingnan Shi, Luca Carlone

TL;DR
This paper introduces TEASER and TEASER++, two fast, certifiable algorithms for robust 3D point cloud registration capable of handling extremely high outlier rates, with theoretical guarantees and superior empirical performance.
Contribution
The paper presents the first certifiable, fast algorithms for robust point cloud registration that effectively handle large outlier fractions using novel optimization and graph-theoretic techniques.
Findings
TEASER and TEASER++ outperform state-of-the-art methods in robustness and speed.
TEASER++ can solve registration problems in milliseconds.
Both algorithms handle over 99% outliers and outperform ICP and Go-ICP.
Abstract
We propose the first fast and certifiable algorithm for the registration of two sets of 3D points in the presence of large amounts of outlier correspondences. We first reformulate the registration problem using a Truncated Least Squares (TLS) cost that is insensitive to a large fraction of spurious correspondences. Then, we provide a general graph-theoretic framework to decouple scale, rotation, and translation estimation, which allows solving in cascade for the three transformations. Despite the fact that each subproblem is still non-convex and combinatorial in nature, we show that (i) TLS scale and (component-wise) translation estimation can be solved in polynomial time via adaptive voting, (ii) TLS rotation estimation can be relaxed to a semidefinite program (SDP) and the relaxation is tight, even in the presence of extreme outlier rates, and (iii) the graph-theoretic framework…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Image and Object Detection Techniques · 3D Surveying and Cultural Heritage
MethodsPruning · Test
